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Building Transcriptional Association Networks in Cytoscape with RegNetC.

Isabel A Nepomuceno-Chamorro, Alfonso Marquez-Chamorro, Jesus S Aguilar-Ruiz

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|September 11, 2015

Summary

The Regression Network plugin for Cytoscape (RegNetC) infers gene association networks using a novel model tree-based algorithm. This approach improves upon correlation methods by analyzing gene relationships simultaneously for more accurate transcriptional network reconstruction.

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Gene expression profiling is crucial for understanding cellular processes.
Correlation-based methods for inferring transcriptional networks have limitations, including analyzing gene pairs individually and favoring global similarities.
Accurate reconstruction of transcriptional association networks is essential for biological discovery.

Purpose of the Study:

To introduce RegNetC, a Cytoscape plugin implementing the RegNet algorithm for transcriptional association network inference.
To provide a user-friendly tool for analyzing gene expression data and reconstructing gene regulatory networks.
To offer an alternative to correlation-based methods with improved accuracy in detecting gene relationships.

Main Methods:

Implementation of the RegNet algorithm, a model tree-based method, within a Cytoscape plugin (RegNetC).
Simultaneous analysis of relationships between each gene and all other genes.
Utilizing regression models within model trees to estimate gene expression values and capture localized similarities.

Main Results:

RegNetC successfully infers transcriptional gene association networks from gene expression profiles.
The plugin generates networks in .sif format, suitable for visualization and analysis within Cytoscape and compatible with other plugins.
Quantitative relationships (regression models) between gene expression values are provided for genes in the inferred network.

Conclusions:

RegNetC offers an effective and integrated software solution for reconstructing transcriptional networks.
The model tree-based approach of RegNetC overcomes limitations of traditional correlation-based methods.
RegNetC facilitates network visualization, analysis, and export for publication, enhancing biological research workflows.